From BOLD-FMRI signals to the prediction of subjective pain perception through a regularization algorithm

Functional magnetic resonance imaging, in particular theBOLD-fMRI technique, plays a dominant role in humanbrain mapping studies, mostly because of its noninvasivenessand relatively high spatio-temporal resolution.The main goal of fMRI data analysis has been to revealthe distributed patterns of brain areas involved in specificfunctions, by applying a variety of statistical methods withmodel-based or data-driven approaches. In the last years,several studies have taken a different approach, where thedirection of analysis is reversed in order to probe whetherfMRI signals can be used to predict perceptual or cognitivestates. In this study we test the feasibility of predicting theperceived pain intensity in healthy volunteers, based on fMRIsignals collected during an experimental pain paradigm lastingseveral minutes. In particular, we introduce a methodologicalapproach based on new regularization learning algorithmsfor regression problems.

Functional magnetic resonance imaging, in particular the BOLD-fMRI technique, plays a dominant role in human brain mapping studies, mostly because of its non-invasiveness and relatively high spatio-temporal resolution. The main goal of fMRI data analysis has been to reveal the distributed patterns of brain areas involved in specific functions, by applying a variety of statistical methods with model-based or data-driven approaches. In the last years, several studies have taken a different approach, where the direction of analysis is reversed in order to probe whether fMRI signals can be used to predict perceptual or cognitive states. In this study we test the feasibility of predicting the perceived pain intensityin healthy volunteers, based on fMRI signals collected during an experimental pain paradigm lasting several minutes. In particular, we introduce a methodological approach based on new regularization learning algorithms for regression problems. © EURASIP, 2009.